A quantum benchmark measures how a device performs on a specified test. A quantum advantage claim goes further: it says a quantum system performs a particular task better than a relevant classical approach, under a defined output-quality target and resource comparison. A strong benchmark score is evidence about the device and test—not, by itself, proof of practical advantage.
What a benchmark can—and cannot—tell you
A benchmark is a measurement protocol. Its result depends on the task, circuits, device conditions, metric and pass criteria chosen by the protocol. A score can help compare performance within that setup, but it does not automatically predict performance on other circuits or useful applications.
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Different benchmarks also examine different parts of a quantum computer. Some estimate errors in gate operations; others exercise more of the software and hardware stack. Treating their scores as interchangeable rankings can obscure what was actually tested.
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Quantum volume: performance on a defined random-circuit test
The quantum volume procedure generates random circuits, runs them on quantum hardware, compares the outcomes with classical simulation, and evaluates the probability of producing so-called heavy outputs: outputs whose probabilities are above the circuit’s median output probability. In the test described by “Re-examining the quantum volume test” (Quantum, May 9, 2022), a benchmark passes when the mean heavy-output probability across the random set exceeds 2/3.
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That pass result concerns the circuits and conditions used for the test. The quantum-volume score compresses performance into one number, so it should not be read as a general-purpose measure of usefulness or as evidence that a machine will solve a practical problem faster. When a result is reported, the tested circuit size and conditions matter alongside the score.
Full-stack benchmarking: behavior across more of the system
Full-stack methods use circuit families designed to exercise more than isolated gate operations. The framework described in “An Effective Framework for Full-Stack Benchmarking of Quantum Computers” (Quantum, April 26, 2021) includes square circuits built from random two-qubit gates with flexible connectivity in the uncompiled circuit. Shallow circuit families can probe different operating regimes.
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These tests can reveal behavior across several layers of a system, but they remain tests of selected circuit families. A result is informative about the workloads and conditions tested; it is not a universal ranking of quantum computers.
Randomized benchmarking: estimating gate-set errors
Standard randomized benchmarking indirectly estimates average error for a gate set by running composite Clifford gates. Its reliance on large multi-qubit Clifford circuits limits the standard approach beyond a few qubits. Converting an error-per-Clifford estimate into an error per native gate can also be unreliable.
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Direct randomized benchmarking is designed to test customizable gate sets more directly. Its reliability still depends on the noise and protocol assumptions. Anthony M. Polloreno and co-authors discuss these issues in “A Theory of Direct Randomized Benchmarking” (Quantum, September 5, 2025). A gate-error estimate and a full-stack circuit result answer different questions; neither should be treated as a substitute for the other.
What a quantum advantage claim must specify
Advantage is a comparison on a task, not a property established by a benchmark score alone. To judge a claim, identify the following:
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- The task: What computation is being performed, and what circuit or problem instance is involved?
- The quantum system: Which device, circuit size, connectivity, depth and noise conditions produced the result?
- The classical comparator: Which classical algorithm and hardware were used, and were they a relevant, strong comparison for this task?
- The output target: What quality, fidelity or sampling target had to be met for the result to count?
- The resources counted: What time, hardware, preprocessing, post-processing and other resources are included on each side?
- The type of claim: Is the result a benchmark pass, a theoretical complexity argument, an experimental quantum-versus-classical separation, or useful application performance?
A quantum device producing samples from a distribution that is difficult for a selected classical method to reproduce may demonstrate an advantage for that sampling task and comparison. It does not establish that quantum computers outperform classical computers on every workload, or that the sampling task itself has immediate practical value.
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Why the classical baseline can change the result
The classical side of a comparison is not fixed: algorithms and hardware improve. Ryan LaRose’s review, “A brief history of quantum vs classical computational advantage” (Quantum, September 1, 2026), surveys experimental claims alongside later challenges, loopholes, refutations, theoretical proposals and progress in quantum error correction. Those are distinct lines of evidence, not a single verdict on the field.
One example cited by the review is the 2024 paper “Leapfrogging Sycamore: Harnessing 1432 GPUs for 7× faster quantum random circuit sampling.” The paper’s title reports the use of 1,432 GPUs and a 7× speedup for quantum random-circuit sampling in its particular comparison. That figure belongs to that task and setup; it is not a general result about quantum computing. The review’s citation alone does not establish the detailed comparison conditions, so the number should not be extended beyond what the paper reports.
A later classical result can narrow or challenge an earlier comparison without showing that all quantum advantage is impossible. Conversely, a theoretical argument about a task’s difficulty is not the same as an experimentally demonstrated separation, and neither alone establishes practical usefulness.
Keep three kinds of result separate
| Kind of result | What it establishes | What it does not establish on its own |
|---|---|---|
| Benchmark result | Performance under a specified protocol, circuit family, metric and device conditions. | Advantage over the best relevant classical approach on a useful application. |
| Experimental quantum-versus-classical claim | A comparison for a defined task, output target and set of quantum and classical resources. | Broad superiority across workloads or lasting advantage against future classical methods. |
| Theoretical hardness argument | A reason, under its assumptions, to expect a classical method to have difficulty with a task. | An experimental speedup or useful real-world application by itself. |
| Application performance | Evidence about performance on a specified practical problem and quality target. | General advantage on unrelated applications. |
The terms “quantum computational advantage” and “quantum supremacy” appear in scholarly discussion. “Quantum supremacy” has sometimes described sampling-based demonstrations, but the label does not mean an application-level advantage has been shown. LaRose’s 2026 review treats experimental demonstrations, theoretical examples and error-correction progress as separate topics.
How to read a new result
- Start with the task and test. Identify the circuit family or computational problem, rather than relying on a headline score or label.
- Locate the measured layer. Check whether the result concerns gate errors, a fuller circuit stack or end-to-end task performance.
- Check the operating conditions. Look for device size, connectivity, circuit depth, noise and the output-quality target.
- Inspect the classical comparison. Find the algorithms, hardware and resources counted, and ask whether the comparison is specific to the stated task.
- Classify the conclusion narrowly. Decide whether the paper reports a benchmark pass, theoretical claim, experimental separation or useful application result. Do not infer one category from another.
The most defensible conclusion is always as specific as the evidence: a device passed a defined benchmark, or a quantum system outperformed a stated classical approach on a stated task under stated conditions. Anything broader needs separate evidence.
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